Last Modified : Saturday, April 20, 2024
Speaker diarization is extracting which user spoke and when.
Need for Automatic Speech Recognition (ASR) is growing exponantionally as they help us extract transcript from an audio file. But a raw transcript from ASR is an unstructed data, to make it more redable we can also extract how many speaker were speaking, which speaker spoke when and for how long. These process is speaker diarization.
How does speaker diarization works?
Simple speaker diarization models usually seprate non-speech metadata and speech metadata. Non-speech metadata contains music, vocals, background noise and others. Sperating these helps them focus only on speech data. Now for every pause in audio file speech models will label it with speaker, if same speaker spoke again then model will detect that and label it.
Why is speaker diarization important?
Speaker diarization improves redability of a transcript, we can understand more about conversation because of this. We will have better summarisation because of speaker diarization. For eg, if you have a meeting recording transcript without speaker labels then you won't understand who said what, but if you have speaker labels then you can understand it better.
What are speaker diarization use cases?
Demand for good quality ASR is increasing as audio transcript can unlock new information, here are few use cases of speaker diarization
- News and broadcast
- Contact centers
- Chatbots and home assistants
- Poadcasts
- Business meetings
- Education
- Legal